Cleaning, Summarizing, and Visualizing Data

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# Cleaning, Summarizing, and Visualizing Data
### Jeroen Mahieu
### <a href="mailto:jeroen.mahieu@vu.nl" class="email">jeroen.mahieu@vu.nl</a>
### Updated 2022-02-09

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# The Data Science Pipeline

* Quantitative Research is about numeric `data`

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# Cleaning (Tidying) Data

* According a to [2014 NYTimes article](https://www.nytimes.com/2014/08/18/technology/for-big-data-scientists-hurdle-to-insights-is-janitor-work.html), "data scientists [...] spend from ***50 percent to 80 percent of their time*** mired in this more mundane labor of collecting and preparing unruly digital data, before it can be explored for useful nuggets."

* Luckily we have some powerful tools to help us out.

* Here, we will focus on [`dplyr`](https://dplyr.tidyverse.org) which is part of the [`tidyverse`](https://www.tidyverse.org)

* (When you work with large datasets (+100k rows with many columns) learn to use [`data.table`](https://github.com/Rdatatable/data.table/wiki) which is much faster but has more difficult syntax)

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# `dplyr` Overview

* You are ***highly encouraged*** to read through [Hadley Wickham's chapter](https://r4ds.had.co.nz/transform.html). It's clear and concise.

* Also check out this great "cheatsheet" [here](https://github.com/rstudio/cheatsheets/blob/master/data-transformation.pdf).

* The package is organized around a set of **verbs**, i.e. *actions* to be taken.

* We operate on `data.frames` or `tibbles` (*nicer looking* data.frames.)